@inproceedings {pub4366,
	title = {Quantifying The Generative Capabilities Of Variational Autoencoders For 3D Car Point Clouds},
	author = {Sneha Saha AND Stefan Menzel AND Leandro L. Minku AND Xin Yao AND Bernhard Sendhoff AND Patricia Wollstadt},
	year = {2020},
	month = {December},
	abstract = {During each cycle of automotive development, large
amounts of geometric data are generated as results of design
studies and simulation tasks. Discovering hidden knowledge from
this data and making it available to the development team
strengthens the design process by utilizing historic information
when creating novel products. To this end, we propose to
use powerful geometric deep learning models that learn low-
dimensional representation of the design data in an unsupervised
fashion. Trained models allow to efficiently explore the design
space, as well as to generate novel designs. One popular class
of generative models are variational autoencoders, which have
however been rarely applied to geometric data. Hence, we
propose a variational autoencoder for 3D point clouds (PC-VAE)
and explore the model{\textquoteright}s generative capabilities with a focus on
the generation of realistic yet novel 3D shapes. We apply the PC-
VAE to point clouds sampled from car shapes from a benchmark
data set and employ quantitative measures to show that our
PC-VAE generates realistic car shapes, wile returning a richer
variety of unseen shapes compared to a baseline autoencoder.
Finally, we demonstrate how the PC-VAE can be guided towards
generating shapes with desired target properties by optimizing
the parameters that maximize the output of a trained classifier
for said target properties. We conclude that generative models
are a powerful tool that may aid designers in automotive product
development.},
	publisher = {IEEE},
	booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}
}